#17 Startup Cuts, Super Agency Headwinds, Fixing Gender in NMTFlorian and Esther discuss the language industry startup environment amid the coronavirus pandemic. As the language industry’s most heavily-funded startup, Unbabel, lays off a third of its staff, others may need to take similar measures to extend their runways given the uncertainty around capital raises in 2020. From startups to stalwarts: Florian shares news from TransPerfect and SDL, which both released financial results this week. TransPerfect had a strong Q1 2020 with 14% growth, while SDL delivered a solid performance across its three divisions — Language Services, Language Technologies, and Content Technologies — in 2019. Looking into summer and beyond, TransPerfect is warning of a softening in Q2, while the first phase of SDL’s mitigation plan is in full swing.Esther discusses a new paper from Cambridge researchers, one of the many papers released ahead of July’s ACL conference. Danielle Saunders and Bll Byrne explored reducing gender bias in neural machine translation using fine-tuning. An evolving topic in machine translation research, and one which is far from solved, gender bias can lead to “identifiable errors” in translations.Links to the stories discussed in this episode:SDL Beats 2019 Forecast, Moves to Cushion Against Pandemichttps://slator.com/financial-results/sdl-beats-2019-forecast-moves-to-cushion-against-pandemic/TransPerfect Posts Strong Q1 2020 but CEO Expects ‘Significant Headwinds’https://slator.com/financial-results/transperfect-posts-strong-q1-2020-but-ceo-expects-significant-headwinds/Cambridge Researchers Tackle Neural Machine Translation’s Gender Biashttps://slator.com/machine-translation/cambridge-researchers-tackle-neural-machine-translations-gender-bias/